Theodore A. Walls University of Rhode Island
Joseph L. Schafer Pennsylvania State University
New York, NY: Oxford University Press
A new class of longitudinal data has emerged as a result of the use of technological devices for data collection in diverse areas of scientific inquiry. For example, social scientific studies frequently utilize hand held computers, beepers, web interfaces and other technological tools for data collection. This class of data is called intensive longitudinal data (ILD) . The volume features state-of-the-art statistical modeling strategies developed by leading statisticians working in conjunction with scientists. Statistical modeling frameworks are outlined and each chapter includes an applied example or application with example programs to be archived on this site.
- Multilevel modeling
- Generalized estimating equations
- Item response theory
- Functional data analysis
- Time series analysis
- Point process modeling
- State space modeling
- Dynamical systems modeling
- Control systems models
- Emerging intensive longitudinal data
- ...and more
This volume is intended for those who want to learn about advanced statistical models for intensive longitudinal data and for those with an interest in selecting and applying a given model. The chapters all take up issues of general concern in modeling these kinds of data, such as a focus on regulatory systems, issues of curve registration, variable frequency and spacing of measurements, complex multivariate patterns of change, and multiple independent series. Specifically, the volume is useful for principal investigators designing new studies that will produce ILD, applied statisticians working on related models, and for methodologists, graduate students and applied analysts working in a range of fields.
For more information or to order the book, please visit Oxford University Press.
Table of Contents:
- Foreword: TBN
- Introduction: Intensive Longitudinal Data. Walls, T.A. & Schafer, J.L.
- Multilevel Models for Intensive Longitudinal Data. Walls, T.A; Jung, H. & Schwartz, J.E.
- Marginal modeling of intensive longitudinal data by generalized estimating equations. Schafer, J.L.
- A local linear estimation procedure for functional multilevel modeling. Li, R., Root, T.L. & Shiffman, S.
- Application of Item Response Theory Models for Intensive Longitudinal Data. Hedeker, D., Mermelstein, R.J. & Flay, B.R.
- Periodic trends, Non-periodic Trends and their Interactions in Longitudinal or Functional Data. Fok, C.C.T. & Ramsay, J.O.
- Multilevel Autoregressive Modeling of interindividual differences in the regularity of a process. Rovine, M.J. & Walls, T.A.
- The State-Space Approach to Modeling Dynamic Processes. Ho, M-H.R., Shumway, R. & Ombao, H.
- The Control of Behavioral Input/Output Systems. Ramsay, J.O.
- Dynamical Systems Modeling: An Application to the Regulation of Intimacy and Disclosure in Marriage. Boker, S.M. & Laurenceau, J-P
- Point Process Models for Event History Data: Applications in the Behavioral Science. Rathbun, S.L.,
- Shiffman, S. & Gwaltney, C.J.
- Emerging Technologies and Next Generation Intensive Longitudinal Data Collection. Nusser, Intille & Maitra